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Data in Motion: Streaming & CDC for AI & Agents
When: 5:30-8:00 PM ( dinner included)
Real-time data is the foundation of modern analytics - & the fuel for AI & agent workflows. Agents can only learn, act, & adapt as fast as the data reaching them, which makes the streaming stack underneath matter more than ever.
Join VeloDB, Redpanda, & Datastrato for an evening on how teams are building that stack: ingesting at scale, moving data with low latency, & querying it the moment it lands - so your analytics, applications, & agents are always working from the freshest context available.
Three short talks, dinner, & conversation in SF!
Speakers
Real-Time Data Architecture for the Agentic Era
Peter Corless - Principal Product Marketing Manager, Redpanda
Discover how enterprises are building enterprise-scale agentic AI applications. These real-time responsive systems require several components in their data architecture: event-driven data streaming, real-time analytics engines, & AI-centric application frameworks for governance, trust, & explainability.
Closing the Governance Gap Between the Stream & the Lakehouse
Mark Hoerth - Product Lead & Solutions Architect, Datastrato
Streaming & analytics keep converging, but governance usually doesn't follow the data across the seam. A topic lives in one world with its own access model; the table it becomes lives in another. This talk shows how an open catalog can span both. Using Redpanda's broker-native Iceberg Topics to turn a live stream into an Apache Iceberg table with no ETL, & Apache Gravitino as the catalog of catalogs holding the topic & resulting table in a single metalake, we'll govern data the same way before & after it lands. The session ends with a live walk from produced records to a governed, queryable Iceberg table under one consistent policy. The takeaway: your governance boundary doesn't have to break where your streaming engine hands off to your lakehouse.
Fast In, Fast Out: A Real-Time Analytics Stack with Apache Iggy (Incubating) & Apache Doris
Kranti, Founder & CEO, LaserData & Kevin Shen, PPM, VeloDB
This talk pairs two open-source projects to build a real-time analytics stack covering both fast ingest & fast queries: Apache Iggy (incubating), LaserData's Rust message-streaming platform that moves high event volumes at very low latency, & Apache Doris (the database behind VeloDB), which serves sub-second queries on fresh data under heavy concurrency, updates, & joins. After a brief intro to each project, the session walks through a Rust-native Iggy sink connector that streams events directly into Doris with no JVM or intermediary systems, then closes with a live demo pushing a real workload through Iggy into Doris & running analytics as the data lands-leaving attendees with an understanding of why real-time analytics needs both halves, how the connector wires them together, & when & how to build the stack themselves.
Important: Building Access Required
This event is hosted at the AWS office. You must register at both links to attend:
RSVP here on Luma
Register for building access: AWS Event Registration
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